How AI And Machine Learning Are Changing Content Delivery Networks

Technological innovation has entered into an exciting era — the twin pair of Machine Learning (ML) and Artificial Intelligence (AI) has generated invigorating discussions in business circles every day around their capabilities and opportunities. These two concepts tend to be paired together, so sometimes confusion can result regarding which is which. Let’s demystify the pair and understand their differences.

Artificial intelligence technology refers to the simulation of human intelligence by machines through learning, self-correcting, and reasoning. Machine learning technology is a crucial subset of AI that relies on feeding computer systems with a large amount of data so that they can learn the intricate relationships between data sets and make effective decisions regarding further data sets. While there are significant overlaps between the two concepts, there are certain areas. that are not common to both of them, such as neural networks. Both ML and AI have developed and matured tremendously over the last decade and are now impacting every business and function. 

Content Delivery Networks (CDNs) are no exception. CDNs play a very crucial role in the online world today, and they are a significant reason behind the rise of OTT and video streaming web services. CDNs help to deliver excellent content delivery and performance; CDNs are also witnessing the impact of significant technological breakthroughs in the fields of AI, ML, and in the migration of network infrastructures to the cloud. These breakthroughs are providing the most significant opportunities for CDN service providers today. 

The demand for website and web application-based content (including video and rich media), as well as streaming content, is on the rise. Hence, CDNs have grown in popularity. CDNs also use innovate cutting-edge technologies to improve response times in order to deliver a seamless and disruption-free content delivery experience via the network, all at a more efficient cost to businesses. 

CDN service providers have to keep developing their technology to cope with increasing content demands, which has resulted in network congestion and heavy loads.  While this is an opportunity for CDNs, high network traffic might lead to disruptions and delays in content provisioning, even with CDNs in place. 

Businesses cannot afford any latency and bad user experiences with content since it is a big factor that leads to user churn. To remain competitive, content services built on web applications will need to lower latency and increase reliability, in order to develop strong loyalty with customers.

The Scope and Impact of AI and ML on CDNs

CDNs cache data across a network of strategically-placed data centers around the globe, so that they may deliver the requested content to the user from the edge server rather than having the request travel to the origin server and then back again. As a result, the user experiences an improved server response time. 

Now the question arises: Which edge server will the CDN direct the request to? A CDN makes this decision with the help of a set of algorithms. These algorithms generate massive data sets, which are stored in server log files. Traditionally, these algorithms tend to be well-defined based on industry standards and they may not be able to respond to the unique and real-time challenges that arise within a network. This is where Machine Learning and Artificial Intelligence can play a pivotal role in making this routing decision smart and efficient. 

With content being pushed to the edge, it becomes essential to infuse network management and optimization with intelligence. ML and AI can be applied to a distributed infrastructure so that network operators are able to proactively identify network traffic patterns and appropriately respond to communications traffic demand so that the users can experience improved content performance. 

To do this, operators gather real-time performance data from the software-defined core and access networks and then use ML and AI algorithms to provide real-time insights that facilitate decision-making. Intelligent solutions built on AI and ML technologies can predict network bottlenecks and proactively re-route traffic to the optimal edge server (or cache server), thereby minimizing any latency.

To do this, ML algorithms are trained with data from corresponding networks to build an appropriate model for the network, so that it can make effective real-time routing decisions. This reduces the chance of small incidents, such as HTTP errors, IO access or cache miss rates, from becoming full-blown issues. Open-source ML and AI also provide an opportunity for CDN service providers to build an intelligent solution without having to invest heavily in technology. 

To leverage the opportunities that AI and ML present, service providers need to build a virtual CDN infrastructure that is embedded with next-generation ML and AI-based solutions. This type of solution will help manage an increasingly complex network effectively and dynamically, provide an intelligent management solution capable of delivering enhanced experiences to content consumers, and unlock new revenue streams for businesses. Data-based learnings and predictive analytics can be used to inform decisions that will grow the bottom line. 

How Will the Industry Benefit?

AI and ML-backed CDNs are promising to deliver better performance at an affordable cost. Hence, there is much excitement among industry-leading organizations about incorporating these innovative technologies to deliver superior network performance and content delivery. Customer experiences with streaming services and content-heavy web applications would benefit immensely from ML and AI technologies applied to network performance data. These applications will help to guide the CDN infrastructure to deliver low-latency and high-quality video applications. This will benefit both the CDN service providers and web application services as a more efficient network usage will be cost-effective to their clients. An intelligent CDN network can also deliver a more streamlined edge server usage, which reduces operations and maintenance costs, and the possibility of server downtime. 

At Medianova, we provide global CDN solutions in streaming, encoding, caching, micro caching, hybrid CDN, and website acceleration. We have delivered and managed CDNs for leading enterprises and our state-of-art solutions are benchmarked against industry-leading quality parameters. 

Get in touch with us to learn more about how Medianova can build and manage an optimized and dedicated CDN for you.